Many machine learning and data mining techniques are designed to operate in devices with sufficient resources to handle large amounts of data and models. With the popularity of mobile devices like smartphones and personal digital assistants (PDAs), the number of applications running on these devices is also increasing rapidly. These devices introduce severe storage and time constraints for any learning algorithm. Typically, a fast online algorithm is required. Moreover, the model needs to be updated continuously since the instance space is limited.
For example, mobile context learning has been pursued under the banners of human-computer interaction (HCI), ubiquitous and pervasive computing. Context is inferred from user activity, the environment, and the state of the mobile device. The model needs to be updated upon receipt of new data. These devices, unlike desktops, do not enjoy abundance of resources and no learner has been designed with the constrained environment of these devices in mind.